The technology research firm Gartner defines Big Data as diverse, high-volume, high-velocity information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.

Big Data Institute defines Big Data as vast data sets that:

• Exhibit variety;
• Include structured, unstructured, and semi-structured data;
• Are generated at high velocity with an uncertain pattern;
• Do not fit neatly into traditional, structured, relational databases
• Can be captured, processed, transformed, and analyzed in a reasonable amount of time only by sophisticated information systems.

Big Data generally consists of the following.

Traditional enterprise data: Examples are customer information from customer relationship management systems, transactional enterprise resource planning data, Web store transactions, operations data, and general ledger data.

Machine-generated/sensor data: Examples are smart meters; manufacturing sensors; sensors integrated into smartphones, automobiles, airplane engines, and industrial machines; equipment logs; and trading systems data.

Social data: Examples are customer feedback comments; microblogging sites such as Twitter; and social media sites such as Facebook, YouTube, and LinkedIn.

Images captured by billions of devices located throughout the world, from digital cameras and camera phones to medical scanners and security cameras.